For details about data description see here
load(file = "../output/mediatenor.Rda")p <- df.reduced %>%
filter(category == "daily_print") %>%
filter(medium != "Berliner") %>%
ggplot(aes(year, wertung, color=p_group, group=p_group)) +
geom_point(size=0.8) + geom_line() +
facet_wrap(~medium, ncol = 3) +
geom_hline(yintercept = 0, color="grey10",
size=0.3, linetype = 2) +
labs(x="", y="", title="Tageszeitungen (ungewichtet)", color="") +
theme(axis.text.x = element_text(angle = 90)) +
scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2))
p## Warning: Removed 23 rows containing missing values (geom_point).
#ggplotly(p, tooltop=c("medium", "wertung"))p <- df.reduced %>%
filter(category == "daily_print") %>%
filter(medium != "Berliner") %>%
ggplot(aes(year, weighted, color=p_group, group=p_group)) +
geom_point(size=0.8) + geom_line() +
facet_wrap(~medium, ncol = 3) +
geom_hline(yintercept = 0, color="grey10",
size=0.3, linetype = 2) +
labs(x="", y="", title="Tageszeitungen (gewichtet)", color="") +
theme(axis.text.x = element_text(angle = 90)) +
scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2))
p## Warning: Removed 23 rows containing missing values (geom_point).
#ggplotly(p, tooltop=c("medium", "weighted"))p <- df.reduced %>%
filter(category == "magazine_print") %>%
ggplot(aes(year, wertung, color=p_group, group = p_group)) +
geom_point(size=0.8) + geom_line() +
facet_wrap(~medium, ncol = 5) +
geom_hline(yintercept = 0, color="grey10",
size=0.3, linetype = 2) +
labs(x="", y="", title="Magazine und Wochenzeitungen (ungewichtet)", color="") +
theme(axis.text.x = element_text(angle = 90)) +
scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2))
p## Warning: Removed 13 rows containing missing values (geom_point).
#ggplotly(p, tooltop=c("medium", "wertung"))p <- df.reduced %>%
filter(category == "magazine_print") %>%
ggplot(aes(year, weighted, color=p_group, group = p_group)) +
geom_point(size=0.8) + geom_line() +
facet_wrap(~medium, ncol = 5) +
geom_hline(yintercept = 0, color="grey10",
size=0.3, linetype = 2) +
labs(x="", y="", title="Magazine und Wochenzeitungen (gewichtet)", color="") +
theme(axis.text.x = element_text(angle = 90)) +
scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2))
p## Warning: Removed 13 rows containing missing values (geom_point).
#ggplotly(p, tooltop=c("medium", "weighted"))p <- df.reduced %>%
filter(category == "news_tv") %>%
ggplot(aes(year, wertung, color=p_group)) +
geom_point(size=0.8) + geom_line() +
facet_wrap(~medium, ncol = 3) +
geom_hline(yintercept = 0, color="grey10",
size=0.3, linetype = 2) +
labs(x="", y="", title="Nachrichtensendungen (ungewichtet)", color="") +
theme(axis.text.x = element_text(angle = 90)) +
scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2))
p## Warning: Removed 4 rows containing missing values (geom_point).
#ggplotly(p, tooltop=c("medium", "wertung"))p <- df.reduced %>%
filter(category == "news_tv") %>%
ggplot(aes(year, weighted, color=p_group)) +
geom_point(size=0.8) + geom_line() +
facet_wrap(~medium, ncol = 3) +
geom_hline(yintercept = 0, color="grey10",
size=0.3, linetype = 2) +
labs(x="", y="", title="Nachrichtensendungen (gewichtet)", color="") +
theme(axis.text.x = element_text(angle = 90)) +
scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2))
p## Warning: Removed 4 rows containing missing values (geom_point).
#ggplotly(p, tooltop=c("medium", "weighted"))p <- df.reduced %>%
filter(category == "polit_tv") %>%
ggplot(aes(year, wertung, color=p_group)) +
geom_point(size=0.8) + geom_line() +
facet_wrap(~medium, ncol = 6) +
geom_hline(yintercept = 0, color="grey10",
size=0.3, linetype = 2) +
labs(x="", y="", title="Politische TV-Shows (ungewichtet)", color="") +
theme(axis.text.x = element_text(angle = 90),
legend.position = "none") +
scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2))
p#ggplotly(p, tooltop=c("medium", "wertung"))p <- df.reduced %>%
filter(category == "polit_tv") %>%
ggplot(aes(year, weighted, color=p_group)) +
geom_point(size=0.8) + geom_line() +
facet_wrap(~medium, ncol = 6) +
geom_hline(yintercept = 0, color="grey10",
size=0.3, linetype = 2) +
labs(x="", y="", title="Politische TV-Shows (gewichtet)", color="") +
theme(axis.text.x = element_text(angle = 90)) +
scale_x_continuous(breaks = seq(min(df.reduced$year),max(df.reduced$year),2))
p#ggplotly(p, tooltop=c("medium", "weighted"))require(ggiraph)
require(ggiraphExtra)radar <- df.reduced %>%
filter(category == "daily_print") %>%
group_by(medium, p_group) %>%
dplyr::summarise(wertung = mean(wertung, na.rm = T)) %>%
ungroup() %>%
spread(key=p_group, value = wertung)radar %>%
ggRadar(aes(color=medium), rescale = F,
alpha = 0, legend.position = "right") +
labs(title = "Tageszeitungen (ungewichtet)")radar <- df.reduced %>%
filter(category == "daily_print") %>%
group_by(medium, p_group) %>%
dplyr::summarise(weighted = mean(weighted, na.rm = T)) %>%
ungroup() %>%
spread(key=p_group, value = weighted)
radar %>%
ggRadar(aes(color=medium), rescale = F,
alpha = 0, legend.position = "right") +
labs(title = "Tageszeitungen (gewichtet)")radar <- df.reduced %>%
filter(category == "magazine_print") %>%
group_by(medium, p_group) %>%
dplyr::summarise(wertung = mean(wertung, na.rm = T)) %>%
ungroup() %>%
spread(key=p_group, value = wertung)
radar %>%
ggRadar(aes(color=medium), rescale = F,
alpha = 0, legend.position = "right") +
labs(title = "Magazine und Wochenzeitungen (ungewichtet)")radar <- df.reduced %>%
filter(category == "magazine_print") %>%
group_by(medium, p_group) %>%
dplyr::summarise(weighted = mean(weighted, na.rm = T)) %>%
ungroup() %>%
spread(key=p_group, value = weighted)
radar %>%
ggRadar(aes(color=medium), rescale = F,
alpha = 0, legend.position = "right") +
labs(title = "Magazine und Wochenzeitungen (gewichtet)")radar <- df.reduced %>%
filter(category == "news_tv") %>%
group_by(medium, p_group) %>%
dplyr::summarise(wertung = mean(wertung, na.rm = T)) %>%
ungroup() %>%
spread(key=p_group, value = wertung)
radar %>%
ggRadar(aes(color=medium), rescale = F,
alpha = 0, legend.position = "right") +
labs(title = "Nachritensendungen (ungewichtet)")radar <- df.reduced %>%
filter(category == "news_tv") %>%
group_by(medium, p_group) %>%
dplyr::summarise(weighted = mean(weighted, na.rm = T)) %>%
ungroup() %>%
spread(key=p_group, value = weighted)
radar %>%
ggRadar(aes(color=medium), rescale = F,
alpha = 0, legend.position = "right") +
labs(title = "Nachritensendungen (gewichtet)")radar <- df.reduced %>%
filter(category == "polit_tv") %>%
group_by(medium, p_group) %>%
dplyr::summarise(wertung = mean(wertung, na.rm = T)) %>%
ungroup() %>%
spread(key=p_group, value = wertung)
radar %>%
ggRadar(aes(color=medium), rescale = F,
alpha = 0, legend.position = "right") +
labs(title = "Politische TV-Shows (ungewichtet)")radar <- df.reduced %>%
filter(category == "polit_tv") %>%
group_by(medium, p_group) %>%
dplyr::summarise(weighted = mean(weighted, na.rm = T)) %>%
ungroup() %>%
spread(key=p_group, value = weighted)
radar %>%
ggRadar(aes(color=medium), rescale = F,
alpha = 0, legend.position = "right") +
labs(title = "Politische TV-Shows (gewichtet)")